Browsing: Qwen

A single 24GB card is the practical floor for serious local inference. It is enough for genuinely capable models, and small enough to sit on one…

Alibaba’s Qwen team has released Qwen3.7-Plus. The model is now available through Alibaba Cloud’s Bailian platform. Bailian is the console international users access as Model Studio.…

Reinforcement learning for language agents is growing more complex. Agents now manage multi-turn tool use, long-running contexts, and multi-agent orchestration. The main engineering challenge is connecting…

Most AI models today are not designed for sustained, multi-step autonomous execution. Tasks like running hundreds of iterative code modifications, or chaining tool calls across hours…

Simultaneous interpretation is one of the harder problems in applied AI. You’re asking a model to translate speech before the speaker has finished a sentence. Every…

Large language models are remarkably capable, yet frustratingly opaque. When a model misbehaves — generating responses in the wrong language, repeating itself endlessly, or refusing safe…

The race to make large language models faster and cheaper to run has largely been fought at two levels: the model architecture and the hardware. But…

Alibaba’s Qwen Team has released Qwen3.6-27B, the first dense open-weight model in the Qwen3.6 family — and arguably the most capable 27-billion-parameter model available today for…

class QwenChat: def __init__(self, model, processor, system=None, tools=None): self.model, self.processor = model, processor self.tokenizer = processor.tokenizer self.history: list[dict] = [] if system: self.history.append({“role”: “system”, “content”: system})…

The open-source AI landscape has a new entry worth paying attention to. The Qwen team at Alibaba has released Qwen3.6-35B-A3B, the first open-weight model from the…